ABSTRACT

Data analysis plays a vital role in guiding medical treatment plans, patient care, and the formulation of control and prevention policies in the field of healthcare. In today's era, researchers in these domains require a firm grasp of data, statistical concepts, and programming skills due to the increasing complexity of data. Reproducible analyses and cutting-edge statistical methods are becoming increasingly necessary.

This book, which is both comprehensive and highly practical, addresses these challenges by laying a solid foundation of data and statistical theory for readers. Subsequently, it equips them with practical skills to conduct analyses using the powerful R programming language, widely used by statisticians. The book takes a gentle approach to help readers navigate data and statistical analysis using R, minimizing the learning curve. RStudio is used as the integrated development environment (IDE) for enhanced productivity for readers to run their R codes.

Following a logical sequence commonly applied in medical and health research, the book covers fundamental concepts of data analysis and statistical modeling techniques. It provides readers, including those with limited statistical knowledge and programming skills, with hands-on experience through R programming.

The online version of this book is available on bookdown.org, a publishing platform provided by RStudio, PBC specifically designed to host books written using the "bookdown" package in R. Additionally, all R codes and datasets in this book can be found on the author's GitHub repository.

chapter Chapter 1|14 pages

R, RStudio and RStudio Cloud

chapter Chapter 2|12 pages

R Scripts and R Packages

chapter Chapter 3|10 pages

RStudio Project

chapter Chapter 4|36 pages

Data Visualization

chapter Chapter 5|18 pages

Data Wrangling

chapter Chapter 6|24 pages

Exploratory Data Analysis

chapter Chapter 7|22 pages

Linear Regression

chapter Chapter 8|22 pages

Binary Logistic Regression

chapter Chapter 9|18 pages

Multinomial Logistic Regression

chapter Chapter 10|30 pages

Poisson Regression

chapter Chapter 12|14 pages

Parametric Survival Analysis

chapter Chapter 13|26 pages

Introduction to Missing Data Analysis

chapter Chapter 14|6 pages

Model Building and Variable Selection